检索结果-内蒙古大学图书馆

Integration of Efficacy Biomarkers Together with Toxicity Endpoints in Immune-Oncology Dose Finding Studies

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Yiding Xu, Zhixing Quan, Hui Lin, Ji Biostatistics and Programming Sanofi 450 Water Street CambridgeMA02141 United States Biostatistics and Programming Sanofi 55 Corporate Drive BridgewaterNJ08807 United States

The primary objective of phase I oncology studies is to establish the safety profile of a new treatment and determine the maximum tolerated dose (MTD). This is motivated by the development of cytotoxic agents based on the underlying assumption that the higher the dose, the greater the likelihood of efficacy and toxicity. However, evidence from the recent development of cancer immunotherapies that aim to stimulate patients’ immune systems to fight cancer challenges this assumption, particularly further escalation after certain dose level might not necessarily increase the efficacy. Dose escalation study of molecular targeted agents (MTA) often does not only rely on the safety profile. In this paper, we propose a simple and flexible model that uses multivariate Gaussian latent variables to integrate toxicity endpoint and efficacy biomarker. This model can be easily extended to incorporate additional immune biomarkers. By simultaneously considering multiple outcomes, the proposed method is better at identifying the biologically optimal dose, which results in better decision-making. Simulation studies showed that the proposed method has desirable operating characteristics by determining the target dose with an optimal risk-benefit trade-off. We have also implemented our proposed method in a user-friendly R Shiny tool. © 2023, CC BY.

关键词： Decision making

Dimension reduction with expectation of conditional difference

学校读者我要写书评

暂无评论

Statistical Theory and Related Fields 2023年第3期7卷 188-201页

作者： Wenhui Sheng Qingcong Yuan Department of Mathematical and Statistical Sciences Marquette UniversityMilwaukeeWIUSA Biostatistics and Programming SanofiUSBridgewaterNJUSA

In this article,we introduce a flexible model-free approach to sufficient dimension reduction analysis using the expectation of conditional difference *** any strict conditions,such as linearity condition or constant covariance condition,the method estimates the central subspace exhaustively and efficiently under linear or nonlinear relationships between response and *** method is especially meaningful when the response is *** also studiedthe√n-consistency and asymptotic normality of the *** efficacy of our method is demonstrated through both simulations and a real data analysis.

关键词： Central subspace expectation of conditionaldifference measure sufficientdimension reduction

A Multi-Arm Two-Stage (MATS) Design for Proof-of-Concept and Dose Optimization in Early-Phase Oncology Trials

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jiang, Zhenghao Mi, Gu Lin, Ji Lorenzato, Christelle Ji, Yuan Department of Statistics University of Chicago 5747 South Ellis Avenue ChicagoIL60637 United States Biostatistics and Programming Sanofi 450 Water Street CambridgeMA02141 United States Biostatistics and Programming Sanofi R&D Vitry-sur-Seine France Department of Public Health Science University of Chicago 5841 South Maryland Avenue ChicagoIL60637 United States

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the status quo of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://***/mats/. Copyright © 2023, The Authors. All rights reserved.

关键词： Oncology

IDENTIFICATION OF PROGNOSTIC AND PREDICTIVE BIOMARKERS IN HIGH-DIMENSIONAL DATA WITH PPLASSO

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhu, Wencan Lévy-Leduc, Céline Ternès, Nils UMR MIA-Paris AgroParisTech INRAE Université Paris-Saclay Paris75005 France Biostatistics and Programming department Sanofi R&D Chilly Mazarin 91380 France

In clinical development, identification of prognostic and predictive biomarkers is essential to precision medicine. Prognostic biomarkers can be useful for anticipating the prognosis of individual patients, and predictive biomarkers can be used to identify patients more likely to benefit from a given treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso (Prognostic Predictive Lasso) integrating prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the Lasso type approaches on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic data from clinical trial RV144. Our method is implemented in the PPLasso R package which is available from the Comprehensive R Archive Network (CRAN). Copyright © 2022, The Authors. All rights reserved.

关键词： Biomarkers

Bayesian methods for the analysis of early-phase oncology basket trials with information borrowing across cancer types

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Jin, Jin Riviere, Marie-Karelle Luo, Xiaodong Dong, Yingwen Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Department of Biostatistics and Programming Research and Development Sanofi Chilly-Mazarin France Department of Biostatistics and Programming Sanofi BridgewaterNJ United States Department of Biostatistics and Programming Oncology Sanofi CambridgeMA United States

Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given the shared aberration, in reality, the treatment effect may vary by cancer type, and potentially only a subgroup of the cancer types respond to the treatment. Various approaches have been proposed to increase the trial power by borrowing information across cancer types, which, however, tend to inflate the type I error rate. In this paper, we review some representative Bayesian information borrowing methods for the analysis of early-phase basket trials. We then propose a novel method called the Bayesian hierarchical model with a correlated prior (CBHM), which conducts more flexible borrowing across cancer types according to sample similarity. We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach and Liu's two-stage approach. Simulation results show that all information borrowing approaches substantially improve the power of independent analysis if a large proportion of the cancer types truly respond to the treatment. Our proposed CBHM approach shows an advantage over the existing information borrowing approaches, with a power similar to that of EXNEX or Liu's approach, but the potential to provide substantially better control of type I error rate. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

Conditional borrowing external data to establish a hybrid control arm in randomized clinical trials

学校读者我要写书评

暂无评论

Journal of Biopharmaceutical Statistics 2022年第6期32卷 954-968页

作者： Hongfei Li Ram Tiwari Qian H. Li a Global Biometrics and Data Sciences Bristol Myers Squibb Princeton New Jersey USAb Department of Statistics University of Connecticut Storrs Connecticut USA a Global Biometrics and Data Sciences Bristol Myers Squibb Princeton New Jersey USA c Department of Biostatistics and Programming Statistics & Data Corporation Tempe Arizona USA

ABSTRACTABSTRACTUtilizing external data from the real world, including data from historical clinical trials, has received increasing interest in drug development. The use of external data to support drug evaluation in clinical trials has mainly been through using various matching methods for baseline characteristics to form external control arms in single-arm trials or to augment control arms of randomized controlled trials in hybrid approaches. However, matching the baseline characteristics between the trial and the external subjects can only guarantee comparability on the level of baseline characteristics. Differences in outcomes between the two data sources may still exist due to contemporaneous and operational characteristics. Similarity between the outcomes in the trial control and the external subjects with similar baseline characteristics can be critical in leveraging the external subjects in the clinical trials. In this paper, a resampling method for augmenting control arms in randomized controlled trials is proposed under the conditional borrowing framework. The new method establishes empirical distributions for the hazard ratio in outcomes between the external and trial control subjects. The borrowing decision is then derived from this empirical distribution using a measure of similarity. Once the borrowing decision is established, the borrowing weights for the external subjects, based on the similarity measure, are incorporated in the weighted partial likelihood to evaluate the treatment effect. The operating characteristics of the hybrid control arm, under both the conditional borrowing and unconditional borrowing frameworks, are evaluated. Simulation is conducted to evaluate Type I error, bias, and power. An illustrative example using simulated data is also presented.

关键词： Conditional borrowing hybrid control real-world data bias Type I error weighted partial likelihood

On multi-armed bandit designs for dose-finding clinical trials

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research

The Journal of Machine Learning Research 2021年第1期22卷 686-723页

作者： Maryam Aziz Emilie Kaufmann Marie-Karelle Riviere Spotify NYC Univ. Lille CNRS Inria Centrale Lille UMR 9189 CRIStAL Lille France Statistical Methodology Group Biostatistics and Programming department Sanofi R&D Chilly-Mazarin France

We study the problem of finding the optimal dosage in early stage clinical trials through the multiarmed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest version of Thompson Sampling, based on a uniform prior distribution for each dose, we provide finite-time upper bounds on the number of sub-optimal dose selections, which is unprecedented for dose-finding algorithms. Through a large simulation study, we then show that variants of Thompson Sampling based on more sophisticated prior distributions outperform state-of-the-art dose identification algorithms in different types of dose-finding studies that occur in phase I or phase I/II trials.

关键词： multi-armed bandits adaptive clinical trials phase I clinical trials phase I/II clinical trials Thompson sampling Bayesian methods

A Classification of Elements of Function Space F(,)

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Soltanifar, Mohsen Analytics Division College of Professional Studies Northeastern University 1400-410 West Georgia Street VancouverBCV6B 1Z3 Canada Biostatistics Division Dalla Lana School of Public Health University of Toronto 620-155 College Street TorontoONM5T 3M7 Canada Biostatistics & Programming Division Biometrics Department ClinChoice Inc 750-2 Robert Speck Parkway MississaugaONL4Z 1H8 Canada

In this paper, we classify the function space of all real-valued functions on R denoted as F (, ) into 28 distinct blocks. Each block contains elements that share common features in terms of the cardinality of their sets of continuity and differentiability. Alongside this classification, we introduce the concept of the Connection, which reveals a special relationship structure between four well-known real-valued functions in real analysis: the Cantor function, Dirichlet function, the Thomae function, and the Weierstrass function. Despite the significance of this field, several perspectives remain *** Codes 05A18, 26A15, 26A21, 26A24, 26A30, 03E10 © 2023, CC BY.

关键词： Functional analysis

GenTwoArmsTrialSize: An R Statistical Software Package to estimate Generalized Two Arms Randomized Clinical Trial Sample Size

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Soltanifar, Mohsen Lee, Chel Hee Shirazi, Amin Behnke, Martha Raymond-Loher, Ilfra Dagne, Getachew A. Analytics Division College of Professional Studies Northeastern University 1400-410 West Georgia Street VancouverBCV6B 1Z3 Canada Department of Mathematics and Statistics University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Statistics College of Liberal Arts and Sciences Iowa State University 2438 Osborn Dr AmesIA50011-1090 United States MPH Biostatistics Program College of Public Health University of South Florida 3010 USF Banyan Circle TampaFL United States Department of Critical Care Medicine Alberta Heath Services University of Calgary 3260 Hospital Dr NW CalgaryABT2N 4Z6 Canada Biostatistics & Programming Division Biometrics Department ClinChoice Inc. 750-2 Robert Speck Parkway MississaugaONL4Z 1H8 Canada Biostatistics & Programming Division Biometrics Department ClinChoice Inc. 11300 Virginia Dr. 4th Floor Fort WashingtonPA19034 United States

The precise calculation of sample sizes is a crucial aspect in the design of clinical trials particularly for pharmaceutical statisticians. While various R statistical software packages have been developed by researchers to estimate required sample sizes under different assumptions, there has been a notable absence of a standalone R statistical software package that allows researchers to comprehensively estimate sample sizes under generalized scenarios. This paper introduces the R statistical software package"GenTwoArmsTrialSize," available on the Comprehensive R Archive Network (CRAN), designed for estimating the required sample size in two-arm clinical trials. The package incorporates four endpoint types, two trial treatment designs, four types of hypothesis tests, as well as considerations for noncompliance and loss of follow-up, providing researchers with the capability to estimate sample sizes across 24 scenarios. To facilitate understanding of the estimation process and illuminate the impact of noncompliance and loss of follow-up on the size and variability of estimations, the paper includes four hypothetical examples and one applied example. The discussion encompasses the package’s limitations and outlines directions for future extensions and *** Codes 62-04, 62-08, 62K05 © 2024, CC BY-NC-SA.

关键词： Software packages